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dc.contributor.authorGhazi, Badih
dc.contributor.authorGolowich, Noah
dc.contributor.authorKumar, Ravi
dc.contributor.authorManurangsi, Pasin
dc.date.accessioned2022-10-21T17:09:21Z
dc.date.available2022-10-21T17:09:21Z
dc.date.issued2021-06-15
dc.identifier.isbn978-1-4503-8053-9
dc.identifier.urihttps://hdl.handle.net/1721.1/145928
dc.publisherACM|Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computingen_US
dc.relation.isversionofhttps://doi.org/10.1145/3406325.3451028en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceACM|Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computingen_US
dc.titleSample-Efficient Proper PAC Learning with Approximate Differential Privacyen_US
dc.typeArticleen_US
dc.identifier.citationGhazi, Badih, Golowich, Noah, Kumar, Ravi and Manurangsi, Pasin. 2021. "Sample-Efficient Proper PAC Learning with Approximate Differential Privacy."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-10-20T14:17:31Z
dc.language.rfc3066en
dc.rights.holderACM
dspace.date.submission2022-10-20T14:17:31Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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